• Journal of Digital Convergence
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• v.18 no.3
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• pp.179-187
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• 2020

In a social environment where population aging is rapidly progressing, the healthcare service market is growing fast with the increasing interest in health and quality of life based on rising income levels and the evolution of technology. In this study, after keywords were extracted from Korean and US patent data published on KIPRIS from 2000 to October 2019, frequency analysis, time series analysis, and keyword network analysis were performed. Through this, the change of technology trends were identified, which keywords related to healthcare was shifted from traditional medical words to ICT words. In addition, although the keywords in Korean patents are 55% similar to those in the US, they show an absolute gap in patent production volume. In the next study, we will analyze various data such as domestic and international research and can obtain meaningful implications in the global market on the identified keywords.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.107-110
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• 2003

The aim of this study is to map the intellectual structure of the field of Organic Light Emitting Diode(OLED). Co-word analysis was employed to reveal patterns and trends in the OLED field by measuring the association strengths of terms representative of relevant publications or other texts produced in OLED field. Data were collected from INSPEC. Important keywords were extracted from author keywords. These author keywords were further standardized. In order to trace the dynamic changes of the OLED field, present the technology mapping. The results show that the OLED field has some established research theme and it also changes rapidly to embrace new themes.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.2
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• pp.91-100
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• 2021

PURPOSE: This study analyzed the trends and characteristics of shoulder rehabilitation research through keyword analysis, and their relationships were modeled using text mining techniques. METHODS: Abstract data of 10,121 articles in which abstracts were registered on the MEDLINE of PubMed with 'shoulder' and 'rehabilitation' as keywords were collected using python. By analyzing the frequency of words, 10 keywords were selected in the order of the highest frequency. Word-embedding was performed using the word2vec technique to analyze the similarity of words. In addition, the groups were classified and analyzed based on the distance (cosine similarity) through the t-SNE technique. RESULTS: The number of studies related to shoulder rehabilitation is increasing year after year, keywords most frequently used in relation to shoulder rehabilitation studies are 'patient', 'pain', and 'treatment'. The word2vec results showed that the words were highly correlated with 12 keywords from studies related to shoulder rehabilitation. Furthermore, through t-SNE, the keywords of the studies were divided into 5 groups. CONCLUSION: This study was the first study to model the keywords and their relationships that make up the abstracts of research in the MEDLINE of Pub Med related to 'shoulder' and 'rehabilitation' using text-mining techniques. The results of this study will help increase the diversifying research topics of shoulder rehabilitation studies to be conducted in the future.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.1
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• pp.253-275
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• 2022

Niklas Luhmann is one of the most contentious and difficult theorist in sociology but follow-up studies on his theory gradually increase for recent 10 years. The purpose of this study is to observe how follow-up studies use the difficult concepts of Luhmann. Unlike previous studies, this study adopted a keyword rather than an article as the unit of analysis because keywords are linguistic constructs that can make concepts observable. The study analyzed co-occurrence of keywords in 139 articles retrieved from social sciences category in Web of Science DB. The key findings were following: the most important keywords were the name of Luhmann(Niklas Luhmann) and theory(social systems); keywords were grouped into 4 clusters(social systems theory, systems theory, legal system and political system, the significant of Luhmann's theory from the viewpoint of the history of social theory); topic terms were systems theory, communication, Autopoiesis, risk, legal system, functional differentiation, environment, social theory, sociological theory, structural coupling, systems and evolution. The significance of the study is following: the study gives keywords as useful access point for beginners of Luhmann's theory; the study proves that content analysis by keywords network can be applied to trend analysis of difficult theoretical researches.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.639-649
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• 2020

This study investigates consumer opinions of clothing care and provides fundamental data to decision-making for oncoming development of clothing care system. Textom, a web-matrix program, was used to analyze big data collected from Naver and Daum with a keyword of "clothing care" from March 2019 to February 2020. A total of 22, 187 texts were shown from the big data collection. Collected big data were analyzed using text-mining, network, and CONCOR analysis. The results of this study were as follows. First, many keywords related to clothing care were shown from the result of frequency analysis such as style, Dryer, LG Electronics, Product, Customer, Clothing, and Styler. Consumers were well recognizing and having an interest in recent information related to the clothing care system. Second, various keywords such as product, function, brand, and performance, were linked to each other which were fundamentally related to the clothing care. The interest in products of the clothing care system were linked to product brands that were also naturally linked to consumer interest. Third, the keywords in the network showed similar attributes from the result of CONCOR analysis that were classified into 4 groups such as the characteristics of purchase, product, performance, and interest. Lastly, positive emotions including goodwill, interest, and joy on the clothing care system were strongly expressed from the result of the sentimental analysis.

• Child Health Nursing Research
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• v.27 no.3
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• pp.201-210
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• 2021

Purpose: This study analyzed research trends related to childhood and adolescent cancer survivors (CACS) using word co-occurrence network analysis on studies registered in the Korean Citation Index (KCI). Methods: This word co-occurrence network analysis study explored major research trends by constructing a network based on relationships between keywords (semantic morphemes) in the abstracts of published articles. Research articles published in the KCI over the past 10 years were collected using the Biblio Data Collector tool included in the NetMiner Program (version 4), using "cancer survivors", "adolescent", and "child" as the main search terms. After pre-processing, analyses were conducted on centrality (degree and eigenvector), cohesion (community), and topic modeling. Results: For centrality, the top 10 keywords included "treatment", "factor", "intervention", "group", "radiotherapy", "health", "risk", "measurement", "outcome", and "quality of life". In terms of cohesion and topic analysis, three categories were identified as the major research trends: "treatment and complications", "adaptation and support needs", and "management and quality of life". Conclusion: The keywords from the three main categories reflected interdisciplinary identification. Many studies on adaptation and support needs were identified in our analysis of nursing literature. Further research on managing and evaluating the quality of life among CACS must also be conducted.

• Journal of Society of Preventive Korean Medicine
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• v.25 no.2
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• pp.33-43
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• 2021

Objectives : This study seeks to grasp the current status of Koryo medical research in North Korea, by focusing on researchers and research topics. Methods : A network analysis of co-authors and keyword which were extracted from Koryo Medicine - a North Korean traditional medicine journal, was conducted. Results : The results of author network analysis was a sparse network due to the low correlation between authors. The domain-wide network density of co-authors was 0.001, with a diameter of 14, average distance between nodes 4.029, and average binding coefficient 0.029. The results of the keyword network analysis showed the keyword "traditional medicine" had the strongest correlation weight of 228. Other keywords with high correlation weight was common acupuncture (84) and intradermal acupuncture(80). Conclusions : Although the co-authors of the Koryo Medicine did not have a high correlation with each other, they were able to identify key researchers considered important for each major sub-network. In addition, the keywords of the Koryo Medicine journals had a very high linkage to herbal medicines.

• Korean Journal of Schizophrenia Research
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• v.23 no.2
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• pp.58-64
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• 2020

Objectives: In this study, we conducted an exploratory analysis of the current media trends on schizophrenia using text-mining methods. Methods: First, web-crawling techniques extracted text data from 575 news articles in 10 major newspapers between 2018 and 2019, which were selected by searching "schizophrenia" in the Naver News. We had developed document-term matrix (DTM) and/or term-document matrix (TDM) through pre-processing techniques. Through the use of DTM and TDM, frequency analysis, co-occurrence network analysis, and topic model analysis were conducted. Results: Frequency analysis showed that keywords such as "police," "mental illness," "admission," "patient," "crime," "apartment," "lethal weapon," "treatment," "Jinju," and "residents" were frequently mentioned in news articles on schizophrenia. Within the article text, many of these keywords were highly correlated with the term "schizophrenia" and were also interconnected with each other in the co-occurrence network. The latent Dirichlet allocation model presented 10 topics comprising a combination of keywords: "police-Jinju," "hospital-admission," "research-finding," "care-center," "schizophrenia-symptom," "society-issue," "family-mind," "woman-school," and "disabled-facilities." Conclusion: The results of the present study highlight that in recent years, the media has been reporting violence in patients with schizophrenia, thereby raising an important issue of hospitalization and community management of patients with schizophrenia.

• Korea Trade Review
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• v.47 no.6
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• pp.189-209
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• 2022

This study aims to analyze Latin America and the Caribbean papers published in Korea during the past 2000-2020 years. Through this study, it is possible to understand the main subject and direction of research in Korea's Latin America and the Caribbean area. As the research mythologies, this study uses the text mining and Social Network Analysis such as frequency analysis, several centrality analyses, and topic analysis. After analyzing the empirical results, there has been a tendency to change the key words and centrality coefficients between 2000-2010 and 2011-2020 years. During 2011-2020 years, the most frequent keywords were changed from Neoliberalism and culture to policy education, and economy related words. The degree and closeness centrality analyses appeared the higher frequency key words. However, the eigenvector centrality appeared very different from the order of frequency key words. The topic analysis shows that the culture, language, and Neoliberalism were the most important keywords during 2000-2010 years but economy, labor trade, industry, development became the most important keywords during 2011-2020 years in topics.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.